For CDAR problems, we argue that these methods can suffer from the under-adaptation and over-adaptation issues since they may ignore the locality information contained in activity data. The locality refers to the fine-grained similarity between two sensor signals that should be considered for domain adaptation. As shown in  REF , domain-level matching completely ignores the intra-domain data structure while class-level matching takes a slightly finer alignment [1]}. However, we find that there exist two un-adapted clusters in activity 2 which means rough matching may result in under-adaptation, i.e., domain- and class-level matching fails to capture the locality information. On the other hand, it is obvious that sample-level methods may be seriously affected by some bad points, such as noisy points or outliers, resulting in over-adaptation, i.e., sample-level matching suffers from overfitting when learning the locality information. In addition, sample-level methods need to match too many points, which is notoriously time-consuming.
